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A Machine Learning Based Model of Boko HaramTemporal Probabilistic Rules and Policy Computation Algorithms

A Machine Learning Based Model of Boko Haram: Temporal Probabilistic Rules and Policy Computation... [In this chapter, we briefly describe the science underlying the temporal probabilistic (TP) rule paradigm for explaining the behavior of terrorist groups such as Boko Haram. We present the syntax and semantics of TP-rules informally, together with a brief sketch of algorithms to extract such rules. Given a set of TP-rules governing the behavior of a group as input, we also present a method to extract policies that prevent attacks by the group.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Machine Learning Based Model of Boko HaramTemporal Probabilistic Rules and Policy Computation Algorithms

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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
ISBN
978-3-030-60613-8
Pages
43 –52
DOI
10.1007/978-3-030-60614-5_3
Publisher site
See Chapter on Publisher Site

Abstract

[In this chapter, we briefly describe the science underlying the temporal probabilistic (TP) rule paradigm for explaining the behavior of terrorist groups such as Boko Haram. We present the syntax and semantics of TP-rules informally, together with a brief sketch of algorithms to extract such rules. Given a set of TP-rules governing the behavior of a group as input, we also present a method to extract policies that prevent attacks by the group.]

Published: Dec 12, 2020

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